Understanding the function of the nervous system requires a sophisticated understanding of its main output, behavior. Although our ability to record from and to manipulate neurons and neural circuits has accelerated at a spectacular pace over the last decade, progress has lagged in coupling the interrogation of the nervous system to similarly high-resolution measures of behavior. As a consequence, we lack a sophisticated understanding of how the brain composes, modifies and controls action. We have recently developed a transformative behavioral characterization technology called Motion Sequencing (MoSeq), which circumvents many of the limitations imposed by typical approaches to behavioral measurement (e.g., overtraining, head-fixation, limited behavioral flexibility). This analytical system works by capturing comprehensive and continuous morphometric data about the three-dimensional (3D) posture of a mouse as it freely behaves. The 3D data are then analyzed using an unsupervised machine learning algorithm to identify patterns of motion that correspond to stereotyped and reused modules of sub-second behavior (which by analogy to natural language we refer to as behavioral ?syllables?). The output of this fitting procedure is a parts list for behavior: a limited set of syllables out of which the rodent creates all of its observable action. In addition, within any given experiment MoSeq identifies the specific transition structure (or ?grammar?) that places individual syllables into sequences; these sequences encode all patterns of spontaneous behavior expressed by an animal in a given experimental context. We have recently combined this behavioral assessment technology with techniques for neural recording, allowing us to assess the relationship between neural activity in behaviorally-relevant circuits and patterns of action. This combined approach allowed us, for example, to identify a code for elemental 3D pose dynamics in striatum; importantly, these observed correlations validate MoSeq as a technology that enables accurate inference of internal states from external states. However, the code that underlies MoSeq is essentially bespoke, inappropriate for distribution, and difficult for all but expert users to navigate. In addition, implementing MoSeq in its current form requires extensive prior mathematical and computational experience, limiting its use to a small set of users with specialized skills. Here we propose Aims to democratize MoSeq by (1) transforming it into an end-to-end pipeline that can be easily used by graduate-student level neuroscientists with minimal expertise, and which can be modified on an ongoing basis to accommodate improvements to MoSeq and (2) to offer hands-on training in the set-up and appropriate use of MoSeq for characterizing behavior and neural-behavioral relationships. Together these aims will create a vibrant community of MoSeq users; the creation of such a group has the potential to transform the way behavior is analyzed across neuroscience, and promises to lead to broad insights into the many and varied relationships between neural circuits and behavior.